Reduction of In-Band Interferences’ Effect Using Subspace Algorithms in Radio Channel Data

Year 2016, Special Issue (2016), 30 - 32, 01.12.2016

Seda Üstün Ercan , Hülya Gökalp

https://doi.org/10.18100/ijamec.270956

Cited By: 1

Abstract

In-band interference increases the noise floor of delay profiles
estimated from frequency modulated continuous wave (FMCW) sounder channel data
and prevents weak multipath components from being detected. In this study two
of the subspace methods named MUltiple SIgnal Classification (MUSIC) and
EigenVector (EV) algorithm were used to reduce the effect of interference in delay
profiles obtained from an FMCW channel data, and the results are compared with
those from conventional FFT method. Results show that MUSIC and EV methods have
similar results for time delay estimation, and perform better than the FFT
method.

Keywords

In-band interference, RF interference, Music, EigenVector, frequency modulated continuous wave

References

• [1] Goppelt M., Blöcher H.-L., Menzel W. Automotive Radar-Investigation of Mutual Interference Mechanisms, Advances in Radio Science, Volume 8, 2010, 2010, pp.55-60.
• [2] Tullsson B. E. Topics in FMWC Radar Disturbance Suppression, IET Conference Publications, 1997.
• [3] Salous S., Gokalp H. Medium And Large Scale Characterization Of UMTS Allocated Frequency Division Duplex Channels, IEEE Transactions on Vehicular Technology, 56, 5, 2007, pp. 2831-2843.
• [4] Brooker G. M., Mutual Interference of Millimeter Wave Radar Systems, IEEE Transactions on Electromagnetic Compatibility, 49, 1, 2007, pp.170-181.
• [5] Schipper T., Harter M., Zwirello L., Mahler T., Zwick T., Systematic approach To Investigate and Counteract Interference-Effects in Automatiive Radars, 9th European Radar Conference (EuRAD), 2012.
• [6] Gokalp H., Taflan G.Y., Salous S. In-Band Interference Reduction in FMCW Channel Data Using Prony Modelling, IET Electronic Letters, 45, 2, 2009, pp. 132-133.
• [7] Gokalp H., Taflan G.Y., Ustun S.,Min-norm Method for Estimating Delay and Doppler Profiles from FMCW Channel Data With In-band Interference, IET Electronic Letters, 40, 11, 2010, pp. 799-800.
• [8] Jinyou Qu, Xun Li, Yijun Wen, A New Method for Weak Signals DOA Estimation in the presence of Strong Interferences”, 2012 IEEE 11th International Conference on Signal Processing, 2012, pp. 320-323,
• [9] Al-Azzo M. F., Al- Sabaawi K. High Resolution Techniques for Direction of Arrival Estimation of Ultrasonic Waves, American Journal of Signal Processing, 2014, pp. 49-59.
• [10] Schmidt R. O., “A Signal Subspace Approach to Multiple Emitter Location and Spectral Estimation”, PhD thesis, Stanford University, Stanford, CA, 1981.
• [11] Youn Y. W., Yi S. H., Hwang D. H., Sun J. H., Kang D. S., Kim Y. H., “MUSIC-based diagnosis algorithm for identifying broken rotor bar faults in induction motors using flux signal”, Journal of Electrical Engineering & Technology, 8.2, 2013, pp. 288-294.
• [12] Chakkor S., Baghouri M., Hajraoui A., “Performance Analysis of Faults Detection in Wind Turbine Generator Based on High-Resolution Frequency estimation Methods”, International Journal of Advanced Computer Science and Applications, 5, 4, 2014, pp. 139-148.
• [13] Gokalp H., Characterization of UMTS FDD Channels, Science and Technology for the degree of Doctor of Philosophy, Department of Electrical Engineering and Electronics, Manchester, United Kingdom, 2001.